Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems - Tapa dura

Tatarenko, Tatiana

 
9783319654782: Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems
Tapa dura
ISBN 10: 3319654780 ISBN 13: 9783319654782
Editorial: Springer, 2017

Sinopsis

This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during communication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space. 



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Acerca del autor

Tatiana Tatarenko received her Ph.D. from the Control Methods and Robotics Lab at the Technical University of Darmstadt, Germany in 2017. In 2011, she graduated with honors in Mathematics, focusing on statistics and stochastic processes, from Lomonosov Moscow State University, Russia. Her main research interests are in the fields of distributed optimization, game-theoretic learning, and stochastic processes in networked multi-agent systems. Currently, Dr. Tatarenko is a research assistant at TU Darmstadt, where she teaches and supervises students. 

De la contraportada

This book presents new efficient methods for optimization in realistic large-scale, multi-agent systems. These methods do not require the agents to have the full information about the system, but instead allow them to make their local decisions based only on the local information, possibly obtained during scommunication with their local neighbors. The book, primarily aimed at researchers in optimization and control, considers three different information settings in multi-agent systems: oracle-based, communication-based, and payoff-based. For each of these information types, an efficient optimization algorithm is developed, which leads the system to an optimal state. The optimization problems are set without such restrictive assumptions as convexity of the objective functions, complicated communication topologies, closed-form expressions for costs and utilities, and finiteness of the system’s state space. 



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Editorial
Springer
Año de publicación
2017
Idioma
Inglés
ISBN 10 
3319654780
ISBN 13 
9783319654782
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
184
Contacto del fabricante
no disponible
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Otras ediciones populares con el mismo título

9783319880396: Game-Theoretic Learning and Distributed Optimization in Memoryless Multi-Agent Systems

Edición Destacada

ISBN 10:  331988039X ISBN 13:  9783319880396
Editorial: Springer, 2018
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